An application of the QM-QSAR method to predict and rationalize lipophilicity of simple monomers

• Published in: Dental materials Vol. 21; no. 7; pp. 591 - 598
• Main Authors: Holder, Andrew J., Ye, Lin, Yourtee, David M., Agarwal, Anjali, Eick, J. David, Chappelow, Cecil C.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.07.2005
• Subjects: AM1; Databases, Factual; Dental Materials - chemistry; Dental Materials - toxicity; Dental restorative; Lipids - chemistry; Lipophilicity; log Po/w; Materials Testing; Models, Chemical; Molecular Structure; Octanols - chemistry; QSAR; Quantitative Structure-Activity Relationship; Quantum Theory; Solubility; Dental restorative; QSAR; AM1; Lipophilicity; log P o/w
• ISSN: 0109-5641; 1879-0097
• DOI: 10.1016/j.dental.2004.08.004

The goal of this study is to develop a model used to predict octanol/water partition coefficients (log P o/w) values for a variety of potential dental materials. In this way, a primary consideration for potential toxicity and a rough estimate of solubility in various environments can be obtained. The AM1 semiempirical quantum mechanical method (in AMPAC) was used to compute chemical data for all compounds in the study. CODESSA then imported the chemical information from AMPAC and computed a large set of informational descriptors. A quantitative structure activity relationship (QSAR) model was derived correlating experimental results from a training set of molecules with certain of the descriptors computed above. A training set of 92 molecules was used to derive the QSAR model and three descriptors were obtained: the molecular surface area, the total dipole moment of the molecule, and FPSA-3 (fractional atom charge weighted partial positive surface area). Various quality indicators were also computed and all fell within acceptable ranges: R 2=0.945; adjusted R 2=0.943; R cv 2 = 0.940 ; variance inflation factors (VIF) for the descriptors above are 1.116, 1.044, and 1.162, respectively. This QSAR model can be used to accurately and rapidly predict log P o/w values for a wide variety of small organic molecules, including potential dental monomers.